Intelligent Interpretation of High-resolution Remote Sensing Images based on Deep Learning
Bibliographic record
Abstract
As an important task of intelligent interpretation research, the object detection of remote sensing images still has many problems to be solved. In this paper, aiming at the characteristics of small-sized object and complex background, in order to solve the problem of poor effect of existing object detection algorithms when applied to remote sensing images, an object detection model of remote sensing images based on the improved Faster R-CNN model is proposed. Based on the original Faster R-CNN model, the feature extraction network VGG16 is improved by designing a feature fusion module. In order to verify the effectiveness of the model in this paper, it is used to carry out experiments on NWPU VHR-10 and DOTA datasets, and mAP has reached 0.886 and 0.810 respectively, which was 6.9% and 11.6% higher than the original Faster R-CNN. The experimental results show that our method effectively improves the object detection effect of remote sensing images, and achieves good results in remote sensing images with small-sized object and complex background.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".